Triple
T4784932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | REGN-EB3 |
E106452
|
entity |
| Predicate | clinicalEffect |
P19730
|
FINISHED |
| Object | reduction of mortality in Ebola virus disease |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: reduction of mortality in Ebola virus disease | Statement: [REGN-EB3, clinicalEffect, reduction of mortality in Ebola virus disease]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clinicalEffect Context triple: [REGN-EB3, clinicalEffect, reduction of mortality in Ebola virus disease]
-
A.
healthEffect
chosen
Indicates the impact or consequence that one entity has on the health or well-being of another.
-
B.
clinicalSignOf
Indicates that one clinical sign is evidence or manifestation of a particular disease, condition, or underlying medical state.
-
C.
primaryEffect
Indicates the main direct outcome or consequence that results from a given cause, action, or condition.
-
D.
hasCommonAdverseEffect
Indicates that two or more entities share at least one adverse effect that occurs in response to them.
-
E.
hasClinicalSignificance
Indicates that something (such as a finding, variant, or condition) has a meaningful impact or relevance in a clinical or medical context.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd43f4a9588190bf73e20bc27c03cc |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd65ae49ec81908f16248d22d1155f |
completed | March 20, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69bd622e1b408190806c15c61519fc74 |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:22 p.m.